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A Hands-on Introduction to Hidden Markov Models

By Tony Weisstein1, Elena Gracheva2, Zane Goodwin2, Zongtai Qi2, Wilson Leung2, Christopher D. Shaffer2, Sarah C.R. Elgin2

1. Truman State University 2. Washington University in St. Louis

A lesson in which students will understand the basic structure of an HMM, the types of data used in ab initio gene prediction, and its consequent limitations.

Listed in Teaching Materials | resource by group Network for Integrating Bioinformatics into Life Sciences Education

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Version 1.0 - published on 04 Jan 2019 doi:10.25334/Q4ZM88 - cite this

Licensed under CC Attribution-NonCommercial-ShareAlike 4.0 International according to these terms

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Description

From the Abstract: In this Lesson, we describe a classroom activity that demonstrates how a Hidden Markov Model (HMM) is applied to predict a eukaryotic gene, focusing on predicting one exon-intron boundary. This HMM lesson is part of the BIOL/CS 370 'Introduction to Bioinformatics' course (Truman State University, MO) and of Bio4342 'Research Explorations in Genomics' (Washington University in St. Louis, MO). The original target student audiences include both Biology and Computer Sciences majors in their junior and senior years, although we believe the model activity would be successful with younger students.

Citation:

Weisstein, A.E., Gracheva, E., Goodwin, Z., Qi, Z., Leung, W., Shaffer, C.D. and Elgin, S.C.R. 2016. A Hands-on Introduction to Hidden Markov Models. CourseSource. https://doi.org/10.24918/cs.2016.8

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